Oakland University William Beaumont School of Medicine
INTRODUCTION Opioid abuse is a serious public health issue and has thrust our country into an opioid overdose epidemic. According to the Centers for Disease Control and Prevention, deaths from prescription opioids have more than quadrupled since 1999. For this reason, it is imperative to improve opioid prescribing practices that reduce exposure to opioids, prevent abuse, and stop addiction. The Michigan Automated Prescription System (MAPS) allows prescribers to track controlled substance utilization in an effort to combat rising overdose deaths. While MAPS was developed, in part, to improve responsible prescribing habits, it has been widely underused by prescribers. Oakland University William Beaumont School of Medicine partnered with the Oakland County Health Division (OCHD) and the Oakland County Prescription Drug Abuse Partnership (OCPDAP) with the overall goal of identifying specific barriers to MAPS utilization by prescribers. METHODS An electronic survey was created in Qualtrics® and emailed to prescribers utilizing pre-existing databases throughout Michigan. All prescribers with a DEA number were eligible to participate. The survey assessed patterns and barriers to use as well as prescriber knowledge of MAPS. The results from the survey will be analyzed using descriptive statistics, T-Tests, and the Pearson’s Chi-Square Test. ANTICIPATED RESULTS It is expected that the results will identify several specific barriers to prescriber utilization, such as a bulky user-interface, absence of real-time data, and lack of integration into electronic medical records. CONCLUSION The results are expected to further our understanding of the barriers that prevent prescriber utilization of MAPS. This will guide OCHD and OCPDAP in the development of toolkits and training programs that can be used to teach prescribers how to more effectively use MAPS. Ultimately, the survey will serve as groundwork for future improvement of MAPS and prescription monitoring here in Michigan.